



import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
    from PIL import Image



class 							lowerCAmelCase_						(    __A				):
       UpperCAmelCase__							:   List[Any]  	=    (
           '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
           '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
           '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
       )
       UpperCAmelCase__							:   int  	=    '''CIDAS/clipseg-rd64-refined'''
       UpperCAmelCase__							:   Tuple  	=    '''image_segmenter'''
       UpperCAmelCase__							:   Optional[Any]  	=    CLIPSegForImageSegmentation

       UpperCAmelCase__							:   Optional[Any]  	=    ['''image''', '''text''']
       UpperCAmelCase__							:   int  	=    ['''image''']
       def __init__(     self,     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Optional[Any]:
          requires_backends(self,     ['vision']			)
          super().__init__(*UpperCamelCase__,     **UpperCamelCase__			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.pre_processor(text=[label],     images=[image],     padding=UpperCamelCase__,     return_tensors='pt'			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						int:


          with torch.no_grad():
             UpperCamelCase       :    Union[str, Any]		      =      self.model(**UpperCamelCase__			).logits
          return logits






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Optional[Any]		      =      outputs.cpu().detach().numpy()
          UpperCamelCase       :    int		      =      0
          UpperCamelCase       :    List[str]		      =      1
          return Image.fromarray((array * 255).astype(np.uinta			)			)











'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator




